LSTM - Aided Speech Enhancement with Wiener Filter Adaptation with Learned Loss Function
Objective
This research focuses on improving speech signal quality using a deep learning-based model. It employs Non-negative Matrix Factorization and Long Short-Term Memory to enhance voice signals, optimizing noise reduction through a learned Wiener filter.
Abstract
Voice augmentation is the process of strengthening voice signals that have been affected by background noise. This work presents a deep learning-based speech signal augmentation model that is innovative. The proposed model consists of two phases: Training (i) and Testing (ii). In the training phase, the noisy input signal is processed using a Non-negative Matrix Factorization (NMF) to estimate the signal and noise spectrums. The Wiener filter's features are then extracted using the Empirical Mean Decomposition (EMD) method. The Fractional Delta AMS characteristics are retrieved, the de-noised signal is obtained from the EMD, and the bark frequency is evaluated. Here, in this paper we’re using “Learning with Learned Loss Function", for improving PESQ score. The key contribution of this study is the precise estimate of the tuning factor η for the Wiener filter using the Long Short Term Memory (LSTM) model for each input signal. The extracted features (EMD), which were trained on the LSTM using a modified wiener filter to break down the spectral input, provide the denoised improved speech signal. A comparative analysis is carried out between the proposed and existing models with respect to error metrics.
Keywords: Voice Augmentation, Non-negative Matrix Factorization (NMF), Empirical Mean Decomposition (EMD), Long Short Term Memory (LSTM), PESQ score.
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Block Diagram
Specifications
Software: Matlab 2020a or above
Hardware:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
· Introduction to Matlab
· What is EISPACK & LINPACK
· How to start with MATLAB
· About Matlab language
· Matlab coding skills
· About tools & libraries
· Application Program Interface in Matlab
· About Matlab desktop
· How to use Matlab editor to create M-Files
· Features of Matlab
· Basics on Matlab
· What is Signal Processing?
· About Signal Processing
· Introduction to Signal Processing
· How analog and digital signal is formed
· Importing the signal via signal acquisition tools
· Analyzing and manipulation of signals.
· Phases of signal processing:
· Acquisition
· Signal enhancement
· Signal restoration
· Medical Signal Processing
· Medical Signal Analysis
· Medical Signal Diagnosis
· Filtering techniques
· Machine Learning Algorithms
· Deep Learning Algorithms etc.
· How to extend our work to another real time applications
· Project development Skills
o Problem analyzing skills
o Problem solving skills
o Creativity and imaginary skills
o Programming skills
o Deployment
o Testing skills
o Debugging skills
o Project presentation skills
o Thesis writing skills
Paper Publishing
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